Update app.py

app.py

@@ -1,59 +1,59 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-from tensorflow import keras
-
-model = keras.models.load_model("Digit.keras")
-
-def preprocess_image(img):
-    if img is None:
-        raise ValueError("No image provided.")
-
-    # If Sketchpad returns a dict, try common keys
-    if isinstance(img, dict):
-        if "image" in img and img["image"] is not None:
-            img = img["image"]
-        elif "composite" in img and img["composite"] is not None:
-            img = img["composite"]
-        else:
-            raise ValueError("No image data found in dictionary.")
-
-    # If still a NumPy array, convert to PIL
-    if isinstance(img, np.ndarray):
-        img = Image.fromarray(img.astype("uint8"))
-
-    # --- MNIST preprocessing ---
-    img = img.convert("L").resize((28, 28))
-    arr = np.array(img).astype("float32")
-    if arr.mean() > 127:  # invert if white background
-        arr = 255 - arr
-    arr = arr / 255.0
-    return arr[np.newaxis, ..., np.newaxis]  # (1,28,28,1)
-
-def predict(img):
-    try:
-        x = preprocess_image(img)
-        probs = model.predict(x, verbose=0)[0]
-        pred = int(np.argmax(probs))
-        return str(pred), {str(i): float(probs[i]) for i in range(10)}
-    except Exception as e:
-        return f"Error: {e}", {}
-
-
-with gr.Blocks(title="MNIST Digit Classifier") as demo:
-    gr.Markdown("## ✍️ Draw a digit (0–9)")
-    with gr.Row():
-        with gr.Column():
-            canvas = gr.Sketchpad(
-                canvas_size=(280, 280),
-                type="pil",                 # <-- forces PIL, avoids dicts
-                label="Draw a digit here"
-            )
-            btn = gr.Button("Predict")
-        with gr.Column():
-            pred_txt = gr.Textbox(label="Predicted Digit", interactive=False)
-            probs = gr.Label(label="Class Probabilities", num_top_classes=10)
-
-    btn.click(predict, inputs=canvas, outputs=[pred_txt, probs])
-
-demo.launch()
+import gradio as gr
+import numpy as np
+from PIL import Image
+from tensorflow import keras
+
+model = keras.models.load_model("Digit.keras")
+
+def preprocess_image(img):
+    if img is None:
+        raise ValueError("No image provided.")
+
+    # If Sketchpad returns a dict, try common keys
+    if isinstance(img, dict):
+        if "image" in img and img["image"] is not None:
+            img = img["image"]
+        elif "composite" in img and img["composite"] is not None:
+            img = img["composite"]
+        else:
+            raise ValueError("No image data found in dictionary.")
+
+    # If still a NumPy array, convert to PIL
+    if isinstance(img, np.ndarray):
+        img = Image.fromarray(img.astype("uint8"))
+
+    # --- MNIST preprocessing ---
+    img = img.convert("L").resize((28, 28))
+    arr = np.array(img).astype("float32")
+    if arr.mean() > 127:  # invert if white background
+        arr = 255 - arr
+    arr = arr / 255.0
+    return arr[np.newaxis, ..., np.newaxis]  # (1,28,28,1)
+
+def predict(img):
+    try:
+        x = preprocess_image(img)
+        probs = model.predict(x, verbose=0)[0]
+        pred = int(np.argmax(probs))
+        return str(pred), {str(i): float(probs[i]) for i in range(10)}
+    except Exception as e:
+        return f"Error: {e}", {}
+
+
+with gr.Blocks(title="MNIST Digit Classifier") as demo:
+    gr.Markdown("## ✍️ Draw a digit (0–9)")
+    with gr.Row():
+        with gr.Column():
+            canvas = gr.Sketchpad(
+                canvas_size=(280, 280),
+                type="pil",                 # <-- forces PIL, avoids dicts
+                label="Draw a digit here"
+            )
+            btn = gr.Button("Predict")
+        with gr.Column():
+            pred_txt = gr.Textbox(label="Predicted Digit", interactive=False)
+            probs = gr.Label(label="Class Probabilities", num_top_classes=10)
+
+    btn.click(predict, inputs=canvas, outputs=[pred_txt, probs])
+
+demo.launch(share=True)